Componentwise approximate Bayesian computation via Gibbs-like steps G Clarté, CP Robert, RJ Ryder, J Stoehr Biometrika 108 (3), 591-607, 2021 | 38 | 2021 |
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields J Stoehr, N Friel Artificial Intelligence and Statistics, 921-929, 2015 | 25 | 2015 |
Faster Hamiltonian Monte Carlo by learning leapfrog scale C Wu, J Stoehr, CP Robert arXiv preprint arXiv:1810.04449, 2018 | 23 | 2018 |
A review on statistical inference methods for discrete Markov random fields J Stoehr arXiv preprint arXiv:1704.03331, 2017 | 22 | 2017 |
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields J Stoehr, P Pudlo, L Cucala Statistics and Computing 25, 129-141, 2015 | 18* | 2015 |
Noisy Hamiltonian Monte Carlo for doubly intractable distributions J Stoehr, A Benson, N Friel Journal of Computational and Graphical Statistics 28 (1), 220-232, 2019 | 13 | 2019 |
Hidden Gibbs random fields model selection using block likelihood information criterion J Stoehr, JM Marin, P Pudlo Stat 5 (1), 158-172, 2016 | 8 | 2016 |
Composite likelihood inference for the Poisson log-normal model J Stoehr, SS Robin arXiv preprint arXiv:2402.14390, 2024 | 4 | 2024 |
GiRaF: a toolbox for Gibbs Random Fields analysis J Stoehr, P Pudlo, N Friel R package version 1 (1), 2020 | 4 | 2020 |
Méthodes de Monte Carlo J STOEHR UNIVERSITÉ PARIS DAUPHINE, Département MIDO Master 1, 2019-2020, 2020 | 4 | 2020 |
Statistical inférence methods for Gibbs random fields J Stoehr HAL 2015, 2015 | 2 | 2015 |
Simulating signed mixtures CP Robert, J Stoehr Statistics and Computing 35 (1), 1-21, 2025 | | 2025 |
Importance sampling-based gradient method for dimension reduction in Poisson log-normal model B Batardière, J Chiquet, J Kwon, J Stoehr arXiv preprint arXiv:2410.00476, 2024 | | 2024 |
A gradient approximation with importance sampling for dimension reduction in natural exponential families B Batardière, J Chiquet, J Kwon, J Stoehr 55èmes journées de Statistiques de la SFdS, 2024 | | 2024 |
Simulating signed mixtures J Stoehr, CP Robert arXiv preprint arXiv:2401.16828, 2024 | | 2024 |
Méthodes d'inférence statistique pour champs de Gibbs J Stoehr Université Montpellier, 2015 | | 2015 |
Poisson lognormal models for count data J Chiquet, M Mariadassou, S Robin, B Batardière, J Kwon, J Stoehr | | |
Component-wise Approximate Bayesian Computation via Gibbs-like steps CPR GRegoire CLARTe, RJ RYDER, J STOEHR | | |
A toolbox for Gibbs Random Fields analysis J Stoehr, P Pudlo, N Friel | | |
Criteres de choix de modele pour champs de Gibbs cachés J Stoehr, JM Marin, P Pudlo | | |